To estimate the pose of each test image using either method, we processed the set of feature correspondences using LO\(^+\) -RANSAC [1]} as implemented in RansacLib [2]}. We used the EPnP algorithm [3]} for local optimization (LO) with a non-minimal set of correspondences. We set the number of LO steps to 10 and the number of least-squares iterations to 10. We performed local optimization from the first iteration, rather than waiting until after 50 iterations as suggested in [1]}, since the number of iterations required by all methods was low. We found that a relatively large inlier threshold of 16 pixels worked well for both methods. The maximum number of iterations was set to 1000. For least-squares refinement we used Levenberg-Marquardt optimization over the re-projection error of the 3D points.
